如何用深度学习推荐电影？教你做自己的推荐系统！

if i in idx_to_movie.keys() and len(str(idx_to_movie[i])) == 6:

movies[i] = (idx_to_movie[i])

movies = filter(lambda imdb: imdb != 0, movies)

total_movies = len(movies)

URL = [0]*total_movies

IMDB = [0]*total_movies

URL_IMDB = {"url":[],"imdb":[]}

i = 0

for movie in movies:

(URL[i], IMDB[i]) = get_poster(movie, base_url)

if URL[i] != base_url+"":

URL_IMDB["url"].append(URL[i])

URL_IMDB["imdb"].append(IMDB[i])

i += 1

# URL = filter(lambda url: url != base_url+"", URL)

df = pd.DataFrame(data=http://ai.51cto.com/art/201705/URL_IMDB)

total_movies = len(df)

import urllib

poster_path = "/Users/wannjiun/Desktop/nycdsa/project_5_recommender/posters/"

for i in range(total_movies):

urllib.urlretrieve(df.url[i], poster_path + str(i) + ".jpg")

from keras.applications import VGG16

from keras.applications.vgg16 import preprocess_input

from keras.preprocessing import image as kimage

image = [0]*total_movies

x = [0]*total_movies

for i in range(total_movies):

image[i] = kimage.load_img(poster_path + str(i) + ".jpg", target_size=(224, 224))

x[i] = kimage.img_to_array(image[i])

x[i] = np.expand_dims(x[i], axis=0)

x[i] = preprocess_input(x[i])

model = VGG16(include_top=False, weights='imagenet')

prediction = [0]*total_movies

matrix_res = np.zeros([total_movies,25088])

for i in range(total_movies):

prediction[i] = model.predict(x[i]).ravel()

matrix_res[i,:] = prediction[i]

similarity_deep = matrix_res.dot(matrix_res.T)

norms = np.array([np.sqrt(np.diagonal(similarity_deep))])

similarity_deep = similarity_deep / norms / norms.T

在代铝闼楝我们起首应用API和IMDB id，大年夜TMDB网站获取片子海报。然后向VGG16供给海报来练习神经收集。最后，用VGG16进修的特点来计算余弦类似性。获得片子类似性之后，我们可以推荐类似度最高的片子。VGG16总共有25088个学来的特点，我们应用这些特点来描述数据集中的每个片子。

来看看应用深度进修的片子推荐体系。

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